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检索条件"任意字段=3rd International Conference on Computer Vision and Image Processing, CVIP 2018"

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3rd international conference on computer vision and image processing, cvip 2018

3rd International Conference on Computer Vision and Image Pr...

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3rd international conference on computer vision and image processing, cvip 2018

ISBN: (纸本)9789813292901

The proceedings contain 38 papers. The special focus in this conference is on computer vision and image processing. The topics include: D2: Real-Time Dehazing in Traffic Video Analytics by Fast Dynamic Bilateral Filtering;Joint Bit Allocation for 3D Video with Nonlinear Depth Distortion—An SSIM-Based Approach;A Modified FCM-Based Brain Lesion Segmentation Scheme for Medical images;word Spotting in Cluttered Environment;physical Intrusion Detection System Using Stereo Video Analytics;identification of Fraudulent Alteration by Similar Pen Ink in Handwritten Bank Cheque;Faster RCNN-CNN-Based Joint Model for Bird Part Localization in images;structural Analysis of Offline Handwritten Mathematical Expressions;l1-Regulated Feature Selection and Classification of Microarray Cancer Data Using Deep Learning;remote Multimodal Biometric Authentication using Visual Cryptography;image Embedding for Detecting Irregularity;optimal Number of Seed Point Selection Algorithm of Unknown Dataset;TexFusionNet: An Ensemble of Deep CNN Feature for Texture Classification;person Identification Using Footprint Minutiae;target Tracking Based Upon Dominant Orientation Template and Kalman Filter;Robust Single image Super Resolution Employing ADMM with Plug-and-Play Prior;indoor–Outdoor Scene Classification with Residual Convolutional Neural Network;Comparison Between LGBP and DCLBP for Non-frontal Emotion Recognition;co-Detection in images Using Saliency and Siamese Networks;hand Down, Face Up: Innovative Mobile Attendance System Using Face Recognition Deep Learning;caption-Based Region Extraction in images;trajectory Classification Using Feature Selection by Genetic Algorithm;action Recognition from Egocentric Videos Using Random Walks;a Bag of Constrained Visual Words Model for image Representation;activity Recognition for Indoor Fall Detection in 360-Degree Videos Using Deep Learning Techniques;a Robust Watermarking Scheme for Copyright Protection;comparison of Reconstruction Methods for Multi

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3rd international conference on computer vision and image processing, cvip 2018

3rd International Conference on Computer Vision and Image Pr...

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3rd international conference on computer vision and image processing, cvip 2018

ISBN: (纸本)9789813290877

The proceedings contain 38 papers. The special focus in this conference is on computer vision and image processing. The topics include: Mushroom Classification Using Feature-Based Machine Learning Approach;deep Learning Framework for Detection of an Illicit Drug Abuser Using Facial image;diagnosis of Prostate Cancer with Support Vector Machine Using Multiwavelength Photoacoustic images;a Deep Learning Framework Approach for Urban Area Classification Using Remote Sensing Data;things at Your Desk: A Portable Object Dataset;storm Tracking Using Geostationary Lightning Observation Videos;pneumonia Detection on Chest X-Ray Using Machine Learning Paradigm;eigenvector Orientation Corrected LeNet for Digit Recognition;retinal-Layer Segmentation Using Dilated Convolutions;vehicle Speed Determination and License Plate Localization from Monocular Video Streams;detecting Face Morphing Attacks with Collaborative Representation of Steerable Features;enabling Text-Line Segmentation in Run-Length Encoded Handwritten Document image Using Entropy-Driven Incremental Learning;fused Spectral Features in Kernel Weighted Collaborative Representation for Gender Classification Using Ocular images;investigation on the Muzzle of a Pig as a Biometric for Breed Identification;integrated Semi-Supervised Model for Learning and Classification;robust Detection of Defective Parts Using Pattern Matching for Online Machine vision System;detection of Down Syndrome Using Deep Facial Recognition;cosaliency Detection in images Using Structured Matrix Decomposition and Objectness Prior;agriculture Parcel Boundary Detection from Remotely Sensed images;a Reference Based Secure and Robust Zero Watermarking System;two-View Triangulation: A Novel Approach Using Sampson’s Distance;linear Regression Correlation Filter: An Application to Face Recognition;human Head Pose and Eye State Based Driver Distraction Monitoring System;preface.

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Proceedings of 3rd international conference on computer vision and image processing 1

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丛书名： Advances in Intelligent Systems and Computing

1000年

作者： Bidyut B. Chaudhuri Masaki Nakagawa Pritee Khanna Sanjeev Kumar

ISBN: (数字)9789813292918

ISBN: (纸本)9789813292901

This book is a collection of carefully selected works presented at the Third international conference on computer vision & image processing (cvip 2018). The conference was organized by the Department of computer Science and Engineering of PDPM Indian Institute of Information Technology, Design & Manufacturing, Jabalpur, India during September 29 - October 01, 2018. All the papers have been rigorously reviewed by the experts from the domain. This 2 volume proceedings include technical contributions in the areas of image/Video processing and Analysis; image/Video Formation and Display; image/Video Filtering, Restoration, Enhancement and Super-resolution; image/Video Coding and Transmission; image/Video Storage, Retrieval and Authentication; image/Video Quality; Transform-based and Multi-resolution image/Video Analysis; Biological and Perceptual Models for image/Video processing; Machine Learning in image/Video Analysis; Probability and uncertainty handling for image/Video processing; andMotion and Tracking.

关键词： Signal, image and Speech processing image processing and computer vision Biometrics

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Proceedings of 3rd international conference on computer vision and image processing 1

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丛书名： Advances in Intelligent Systems and Computing

2020年

作者： Bidyut B. Chaudhuri Masaki Nakagawa Pritee Khanna Sanjeev Kumar

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Hybrid Attention vision Transformer-based Deep Learning Model for Video Caption Generation 3

Hybrid Attention Vision Transformer-based Deep Learning Mode...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Ansari, Khustar Srivastava, Priyanka Sarala Birla University Jharkhand Ranchi835103 India Bit Sindri Dhanbad Department of Computer Science and Engineering Jharkhand Sindri828123 India Sarala Birla University Computer Science and Engineering Jharkhand Ranchi835103 India

ISBN: (纸本)9798331509675

Video caption generation is an extremely challenging computer vision task that automatically generates the caption for video clips with a clear understanding of the embedded semantics. Due to the extensive use of images for conveying large volumes of information, there exists a huge demand for image analytics in information processing systems. Various techniques are designed for automatic image caption generation to solve the computer vision challenges, but highlighting the contextual information more accurately still results in a challenging task. To overcome such issues, a Hybrid Attention vision Transformer-based Convolutional Neural Network-Gated Recurrent Unit-Bidirectional Long Short-Term Memory (HAVisT-CNN-GRU-BiLSTM) is developed for video caption generation. Specifically, the vision transformer (VisT) effectively captures the global dependencies and learns the intricate relationships present in the image. Moreover, the proposed approach combines the salient information extracted from the CNN, GRU, and BiLSTM models resulting in less memory usage, high dimensional data processing, and improved convergence speed. Moreover, the hybrid attention-based vision transformer facilitates capturing the global context information and enhances computational efficiency. The experimental results demonstrate that the HAVisT-CNN-GRU-BiLSTM model achieves the efficient metric values of BLEU, METEOR, and ROUGE as 0.48, 0.28, and 0.71 respectively outperforming the other existing techniques. © 2025 IEEE.

关键词： Convolutional neural networks

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Design of an Automatic Agricultural Machinery Signage Recognition System Based on computer vision 24

Design of an Automatic Agricultural Machinery Signage Recogn...

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3rd international conference on Algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Liu, Siqi South China University of Technology Guangdong Guangzhou China

ISBN: (纸本)9798400718120

[Objective] This study aims to design an automatic agricultural machinery identification system based on computer vision, to address the issue of disorganized and chaotic labeling of some agricultural machinery products in the current market, thereby enhancing user recognition efficiency and corporate brand image. [ Methods: ]High-resolution cameras were used to capture images of agricultural machinery under various lighting and weather conditions. image quality was enhanced through techniques such as histogram equalization and adaptive contrast enhancement. Convolutional Neural Networks (CNN) were utilized for feature extraction and classification recognition. The system architecture integrates an image processing module, feature extraction module, classifier module, and user interface to ensure overall system efficiency and user-friendliness. [Conclusion and Results] The results indicate that the system can accurately identify agricultural machinery labels in complex environments, significantly improving recognition accuracy and speed, and meeting the needs for real-time processing. The design method based on visual impact effects has significant application value in the identification of agricultural machinery labels, effectively enhancing the market competitiveness and user satisfaction of agricultural machinery products. © 2024 Copyright held by the owner/author(s).

关键词： Machine vision

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Advancing Deep Learning on Edge Devices: Fine-Tuning and Deployment of YOLOv7 Model for Efficient Object Detection in AI based computer vision Applications 3

Advancing Deep Learning on Edge Devices: Fine-Tuning and Dep...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Shekhar, Sudhanshu Sathwik, T.S. Pritwani, Mayank Mohana Ramakanth Kumar, P. Sreelakshmi, K. RV College of Engineering® Bengaluru India

ISBN: (纸本)9798331527549

This paper investigates the optimization and deployment of YOLOv7 deep learning model on NVIDIA Jetson Nano, an AI-focused edge computing platform for object detection in various computer vision applications. The work leverages TensorRT and quantization techniques for model acceleration for good detection accuracy. Further it examines performance metrics such as speed, accuracy, and resource utilization for image dataset. The model is trained using 80 different classes of objects and demonstrates the use of 6 classes. The average detection accuracy obtained 92.35% and the average processing time is 117.8ms. This work supports AI by demonstrating the feasibility of running deep learning models on edge devices and provides insight into the challenges and opportunities of optimizing AI models for energy-efficient, real-time operations on edge devices for various computer vision applications. © 2025 IEEE.

关键词： Adversarial machine learning

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Video enabled crowd monitoring system and analysis 3

Video enabled crowd monitoring system and analysis

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Mehra, Gunjan Joshi, Aditya Singh, Aakriti Shuaib, Mohd Diwakar, Manoj Singh, Prabhishek Graphic Era Deemed to be University Department of Computer Applications Dehradun India Graphic Era Deemed to be University Department of Cse Dehradun India School of Computer Science Engineering & Technology Bennett University Greater Noida India

ISBN: (纸本)9798331515911

One of the most victorious strategies for slowing the spread of the COVID-19 virus has been proposed: social isolation. A computer vision-based AI-assisted approach for aiding with social distance norms compliance is utilized. The solution consists of modules for person detection, tracking, and distance violations detection. You are given the option to select a tool-based mode for camera calibration. In this paper, the various measures for determining the likelihood of social distance violations and how to tell them apart from temporary violations from persistent ones are demonstrated. On countless real-world issues, AI/Deep Learning has in the past shown promising results. In this paper, how to observe crowds in public areas and workplaces using Python, image processing, and computer vision is discussed in depth. © 2025 IEEE.

关键词： Deep learning

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Smart Animal Monitoring System for Farms using YOLO and OpenCV 3

Smart Animal Monitoring System for Farms using YOLO and Open...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Krishnaveni, A. Arvinth, K.A. Karpagam Academy of Higher Education Deemed to be University Department of Computer Science Coimbatore641 021 India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Animal detection in agricultural environments is a crucial component of modern farming practices, enabling farmers to monitor livestock, enhance security, and optimize resource management. This paper proposes a solution for animal detection in farm settings, leveraging the speed and accuracy of the You Only Look Once (YOLO) object detection algorithm and the versatile computer vision capabilities of the Open-Source computer vision Library (OpenCV). Utilizing the YOLO algorithm, recognized for its real-time object detection capabilities, enables the identification and localization of animals in farm images or video frames. OpenCV complements this process by providing a robust framework for image processing, enabling tasks such as image preprocessing, feature extraction, and camera interfacing. Integrating YOLO and OpenCV aims to create an efficient and reliable system for realtime animal detection, offering farmers valuable insights into livestock activities and facilitating prompt decision-making. The proposed approach helps to enhance farm management applications, contributing to increased productivity and addressing challenges related to animal monitoring in agricultural areas. The proposed work has been implemented using a Python tool, and the results gained 98% accuracy. © 2025 IEEE.

关键词： Livestock

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A Performance Comparison of Multiple Deep Learning Methodologies in image to Text Synthesis Tasks 3

A Performance Comparison of Multiple Deep Learning Methodolo...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Martinez, Jude Joseph Lamug Winata, Edward Kevin Bina Nusantara International University Computer Science Department Faculty of Computing and Media Central Jakarta11480 Indonesia

ISBN: (纸本)9798331509675

Generating textual descriptions from visual inputs has become a critical area of research in the intersection of computer vision and natural language processing called image captioning. This study deep dives towards comparing a few popular image captioning methodologies, going from traditional encoder decoder architectures to more advanced techniques that incorporate attention mechanisms, transformers, and reinforcement learning. We evaluate the performance of these models on publicly available datasets containing logs from the online community Flickr on several dimensions including caption accuracy, diversity and computational efficiency. Captions are evaluated in terms of both quantitative metric(BLEU, ROUGE-N, METEOR, CIDEr, SPICE) and qualitative analysis(contextual relevance besides creativity) for the caption quality. The results show clear advantages and trade offs of each method, allowing for a holistic assessment of how each approach is suitable for real world problems. Thus, this work sheds important light on the trade offs between model complexity, efficiency and performance in image captioning tasks and provides a useful reference for researchers making similar decisions about model design in similar tasks. © 2025 IEEE.

关键词： Deep reinforcement learning

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